Tempo tracking and rhythm quantization by sequential Monte Carlo

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

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Ali Taylan Cemgil, Bert Kappen


We present a probabilistic generative model for timing deviations in expressive music. performance. The structure of the proposed model is equivalent to a switching state space model. We formu(cid:173) late two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as fil(cid:173) tering and maximum a posteriori (MAP) state estimation tasks. The inferences are carried out using sequential Monte Carlo in(cid:173) tegration (particle filtering) techniques. For this purpose, we have derived a novel Viterbi algorithm for Rao-Blackwellized particle fil(cid:173) ters, where a subset of the hidden variables is integrated out. The resulting model is suitable for realtime tempo tracking and tran(cid:173) scription and hence useful in a number of music applications such as adaptive automatic accompaniment and score typesetting.